# SK-ResNeXt

**SK ResNeXt** is a variant of a [ResNeXt](https://www.paperswithcode.com/method/resnext) that employs a [Selective Kernel](https://paperswithcode.com/method/selective-kernel) unit. In general, all the large kernel convolutions in the original bottleneck blocks in ResNext are replaced by the proposed [SK convolutions](https://paperswithcode.com/method/selective-kernel-convolution), enabling the network to choose appropriate receptive field sizes in an adaptive manner.

## How do I use this model on an image?

To load a pretrained model:

```py
>>> import timm
>>> model = timm.create_model('skresnext50_32x4d', pretrained=True)
>>> model.eval()
```

To load and preprocess the image:

```py 
>>> import urllib
>>> from PIL import Image
>>> from timm.data import resolve_data_config
>>> from timm.data.transforms_factory import create_transform

>>> config = resolve_data_config({}, model=model)
>>> transform = create_transform(**config)

>>> url, filename = ("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg")
>>> urllib.request.urlretrieve(url, filename)
>>> img = Image.open(filename).convert('RGB')
>>> tensor = transform(img).unsqueeze(0) # transform and add batch dimension
```

To get the model predictions:

```py
>>> import torch
>>> with torch.no_grad():
...     out = model(tensor)
>>> probabilities = torch.nn.functional.softmax(out[0], dim=0)
>>> print(probabilities.shape)
>>> # prints: torch.Size([1000])
```

To get the top-5 predictions class names:

```py
>>> # Get imagenet class mappings
>>> url, filename = ("https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt", "imagenet_classes.txt")
>>> urllib.request.urlretrieve(url, filename) 
>>> with open("imagenet_classes.txt", "r") as f:
...     categories = [s.strip() for s in f.readlines()]

>>> # Print top categories per image
>>> top5_prob, top5_catid = torch.topk(probabilities, 5)
>>> for i in range(top5_prob.size(0)):
...     print(categories[top5_catid[i]], top5_prob[i].item())
>>> # prints class names and probabilities like:
>>> # [('Samoyed', 0.6425196528434753), ('Pomeranian', 0.04062102362513542), ('keeshond', 0.03186424449086189), ('white wolf', 0.01739676296710968), ('Eskimo dog', 0.011717947199940681)]
```

Replace the model name with the variant you want to use, e.g. `skresnext50_32x4d`. You can find the IDs in the model summaries at the top of this page.

To extract image features with this model, follow the [timm feature extraction examples](../feature_extraction), just change the name of the model you want to use.

## How do I finetune this model?

You can finetune any of the pre-trained models just by changing the classifier (the last layer).

```py
>>> model = timm.create_model('skresnext50_32x4d', pretrained=True, num_classes=NUM_FINETUNE_CLASSES)
```
To finetune on your own dataset, you have to write a training loop or adapt [timm's training
script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py) to use your dataset.

## How do I train this model?

You can follow the [timm recipe scripts](../scripts) for training a new model afresh.

## Citation

```BibTeX
@misc{li2019selective,
      title={Selective Kernel Networks}, 
      author={Xiang Li and Wenhai Wang and Xiaolin Hu and Jian Yang},
      year={2019},
      eprint={1903.06586},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}
```

<!--
Type: model-index
Collections:
- Name: SKResNeXt
  Paper:
    Title: Selective Kernel Networks
    URL: https://paperswithcode.com/paper/selective-kernel-networks
Models:
- Name: skresnext50_32x4d
  In Collection: SKResNeXt
  Metadata:
    FLOPs: 5739845824
    Parameters: 27480000
    File Size: 110340975
    Architecture:
    - Convolution
    - Dense Connections
    - Global Average Pooling
    - Grouped Convolution
    - Max Pooling
    - Residual Connection
    - Selective Kernel
    - Softmax
    Tasks:
    - Image Classification
    Training Data:
    - ImageNet
    Training Resources: 8x GPUs
    ID: skresnext50_32x4d
    LR: 0.1
    Epochs: 100
    Layers: 50
    Crop Pct: '0.875'
    Momentum: 0.9
    Batch Size: 256
    Image Size: '224'
    Weight Decay: 0.0001
    Interpolation: bicubic
  Code: https://github.com/rwightman/pytorch-image-models/blob/a7f95818e44b281137503bcf4b3e3e94d8ffa52f/timm/models/sknet.py#L210
  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/skresnext50_ra-f40e40bf.pth
  Results:
  - Task: Image Classification
    Dataset: ImageNet
    Metrics:
      Top 1 Accuracy: 80.15%
      Top 5 Accuracy: 94.64%
-->